Estimation of Total Organic Carbon in Source Rocks by Using Back-propagation Artificial Neural Network and Passay Method-A Case Study

Abstract

The purpose of this study is to calculate Total Organic Carbon (TOC) values of the Iranian field using a combination of sonic and resistivity logs (Passay method) and neural networks method in the conditions, where the core analysis or well-log measurement does not exist. We compared the resultant TOC with the ones obtained from the geochemical analysis. To correlate between the total organic carbon data and petrophysical log, which are available after logging, Multilayer Perceptron Artificial Neural Network is used. After analyzing 100 cutting samples by using rock -Eval pyrolysis, geochemical parameters have achieved. By using the multi-layer perceptron with Levenberg–Marquardt training algorithm, the TOC with correlation coefficient 0.88 and MSE 1.443 have been provided in the intervals without analyzed samples. Finally, the TOC was estimated by using separation of resistivity and the sonic log, although, with the favorable results in some other fields, the estimation had a correlation coefficient of 51% in this field. Comparing the performance of the multi-layer perceptron with Levenberg–Marquardt training algorithm (with an accuracy of 88%) and results of the Passay method (with an accuracy of 51%) indicated that the neural network is more accurate and has better consistency compared with the empirical formula.